Automatic threshold setting and baseline determination for real-time PCR

ABSTRACT

The invention discloses a system and methods for quantitating the presence of nucleic acid sequences by evaluation of amplification data generated using real-time PCR. In one aspect, the methods may be adapted to identify a threshold and threshold cycle for one or more reactions based upon evaluation of exponential and baseline regions for each amplification reaction. The methodology used in the analysis may be readily automated such that subjective user interpretation of the data is substantially reduced or eliminated.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 10/152,280, filed May 20, 2002, which claims priority to U.S.Provisional Patent Application No. 60/355,661, entitled “AutomaticThreshold Setting And Baseline Determination For Quantitative PCR” filedFeb. 7, 2002 which is hereby incorporated by reference. Additionally,this application incorporates by reference co-pending application U.S.patent application Ser. No. 10/155,877, entitled “Automatic ThresholdSetting For Quantitative Polymerase Chain Reaction.”

BACKGROUND

The invention generally relates to nucleic acid analysis, and moreparticularly, to a system and methods for evaluating results obtainedfrom quantitative amplification reactions.

DESCRIPTION OF THE RELATED ART

Quantitative nucleic acid analysis is extensively used in biologicalresearch and clinical analysis. Some of the applications which make useof this technology include: measurement of gene expression, monitoringof biological responses to stimuli, genomic-level gene quantitation, andpathogen detection. Typically, these methodologies utilize PolymeraseChain Reaction (PCR) as a means for selectively amplifying nucleic acidsequences in a manner that allows for their detection. While it isgenerally desirable to automate the quantitation process, conventionalmethodologies often require a degree of user input in the form ofsubjective interpretation and/or approximation. As a result, thesetechniques may suffer from reduced accuracy and significant user-inducedvariability. Furthermore, in high-throughput applications where manysamples are to be processed simultaneously, it is desirable to provideincreased automation capabilities to improve the speed with which theanalysis may be conducted. The aforementioned limitations ofconventional techniques illustrate the need for an improved method foranalyzing data generated by PCR-based quantitation techniques that mayincrease the potential for automation while improving the quantitativeaccuracy and reproducibility of the analysis.

SUMMARY

In one embodiment, the invention comprises a system and methods forprocessing and evaluating data generated in real-time quantitative PCR.During the amplification reaction, fluorescence intensity signals areacquired that form an amplification profile from which an exponentialamplification region is desirably identified. In determining theexponential region, the invention determines the upper and lower boundswhere more efficient amplification takes place and identifies a baselineused to estimate and compensate for noise. Subsequently, a threshold andthreshold cycle are determined which may be used to quantitate theinitial target concentration present at the onset of the amplificationreaction.

In another embodiment, the invention comprises a method for quantifyingnucleic acid sequences present in one or more amplification reactions tobe collectively analyzed. The method further comprising the steps of:(a) acquiring intensity data for each reaction over a selected number ofreaction intervals wherein the intensity data is indicative of adetected quantity of progeny sequences arising from each sequence; (b)assessing the intensity data over the selected number of reactionintervals to generate an amplification profile indicative of the changein quantity of the progeny sequences for each reaction interval; (c)evaluating each amplification profile to identify a correspondingexponential region, having upper and lower bounds; (d) determining athreshold based upon an intersection between at least one exponentialregion upper bound with at least one exponential region lower bound; (e)performing a polynomial fitting operation for each amplification profilethat applies the threshold to determine a polynomial root which isthereafter associated with a threshold cycle for each reaction; and (f)quantifying the sequence for each reaction using the threshold cycle.

In still another embodiment, the invention comprises a method forquantitating at least one nucleic acid target of unknown concentration.The method further comprising the steps of: (a) performing PCR-basedamplification of each target using a detectable reporter construct; (b)acquiring detection information generated by the detectable reporterconstruct indicative of a change in the concentration of each targetover the course of the amplification; (c) assembling a data setcomprising at least a portion of the detection information to modelamplification reaction characteristics; (d) identifying an exponentialregion for each target of the data set from the modeled amplificationreaction characteristics; (e) identifying a baseline component based, inpart, on the exponential region; (f) normalizing the data set using thebaseline component; (g) determining a threshold based upon a comparisonof the exponential regions for the targets of the data set; (h)identifying a polynomial equation whose root is identified using thethreshold and wherein the root is assigned as a threshold cycle; and (i)quantifying each target using the threshold cycle.

In a still another embodiment, the invention comprises a system foranalyzing quantitative amplification data. The system further comprisesa reaction module, a data collection module, and a data processingmodule wherein: The reaction module used to perform PCR amplification ofat least one sample target using a detectable reporter label; The datacollection module that detects reporter label intensities over thecourse of the PCR amplification for the at least one sample target; Thedata processing module configured to: (a) receive the detectedintensities for each sample target and subsequently generate acorresponding amplification profile to model the PCR amplification forthe sample target; (b) identify an exponential region for eachamplification profile, each exponential region further having upper andlower bounds; (c) identify a characteristic equation for eachamplification profile based, in part, from the lower bound of theexponential threshold, and thereafter generate a normalizedamplification profile using the characteristic equation; and (d)identify a threshold and threshold cycle using the normalizedamplification profile.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects, advantages, and novel features of the inventionwill become apparent upon reading the following detailed description andupon reference to the accompanying drawings. In the drawings, similarelements have similar reference numerals.

FIG. 1 illustrates an exemplary amplification plot for a quantitativePCR reaction.

FIG. 2 is a flowchart illustrating an overview of an amplification dataanalysis method.

FIG. 3A is a flowchart illustrating one embodiment of a method forexponential region determination.

FIGS. 3B-D illustrate exemplary data analysis graphs for exponentialregion identification.

FIG. 4 is a flowchart illustrating one embodiment of a baselinedetermination method.

FIG. 5 is a flowchart illustrating one embodiment of a thresholddetermination method.

FIG. 6 is a flowchart illustrating one embodiment of a threshold cycleselection method.

FIGS. 7A-D are diagrams illustrating the threshold cycle selectionmethod applied to a plurality of amplification profiles.

FIG. 8 is a block diagram of a quantitative PCR system incorporating anautomated threshold detection module.

FIG. 9 is an exemplary pseudo-code implementation of the threshold andthreshold cycle identification methods.

DETAILED DESCRIPTION OF THE CERTAIN EMBODIMENTS

Reference will now be made to the drawings wherein like numerals referto like elements throughout. As used herein, “target”, “targetpolynucleotide”, and “target sequence” and the like refer to a specificpolynucleotide sequence that is the subject of hybridization with acomplementary polynucleotide, e.g., a blocking oligomer, or a cDNA firststrand synthesis primer. The target sequence can be composed of DNA,RNA, analogs thereof, or combinations thereof. The target can besingle-stranded or double-stranded. In primer extension processes, thetarget polynucleotide which forms a hybridization duplex with the primermay also be referred to as a “template.” A template serves as a patternfor the synthesis of a complementary polynucleotide (Concise Dictionaryof Biomedicine and Molecular Biology, (1996) CPL Scientific PublishingServices, CRC Press, Newbury, UK). A target sequence for use with thepresent invention may be derived from any living or once livingorganism, including but not limited to prokaryote, eukaryote, plant,animal, and virus, as well as synthetic and/or recombinant targetsequences.

Furthermore, in describing the invention, as used herein thepolynucleotide sequence may refer to a polynucleotide chain of variablelength and may comprise RNA, DNA, cRNA, cDNA, or other polynucleotidespecies including but not limited to analogs having other than aphosphodiester backbone. Furthermore, as used herein, “reactioninterval” refers to a designated portion of a target amplificationreaction and may be evaluated as a function of cycle number or reactiontime. Additionally, as used herein, “intensity data” refers to ameasured or observed signal generated during the amplification reactionwhich may be related to the amount of target in the reaction and maycomprise fluorescent measurements, radiolabel measurements, electricalmeasurements, light emission measurements, and other types of signalsand measurements generated and acquired during the amplificationreaction.

In general, amplification of a target DNA strand by polymerase chainreaction (PCR) proceeds through a series of temperature regulated cyclesusing the activity of a thermostable enzyme and a sequence specificprimer set. At an appropriate temperature, primers hybridize to portionsof the DNA strand and the enzyme successively adds a plurality ofnucleotide bases to elongate the primer resulting in the production ofprogeny (daughter) strands. Each progeny strand possesses acomplimentary composition relative to the target strand from which itwas derived and can serve as a target in subsequent reaction cycles.

When applying quantitative methods to PCR-based technologies, afluorescent probe or other detectable reporter construct may beincorporated into the reaction to provide a means for determining theprogress of the target amplification. In the case of a fluorescentprobe, the reaction can be made to fluoresce in relative proportion tothe quantity of nucleic acid product produced. The TaqMan®. procedure(Applied Biosystems, Calif.) describes one such fluorescent methodologyfor performing quantitative PCR.

Briefly described, the TaqMan®. system integrates the use of adetectable reporter construct which comprises both a fluorescent labelmolecule and a quencher molecule. As long as the reporter constructremains intact, fluorescent label molecule emissions are absorbed by thequencher molecule. During the amplification process, however, thereporter construct is cleaved and the quencher molecule is releasedallowing the fluorescent label molecule emissions to be detected. Thequantity or intensity of observed fluorescence may then be correlatedwith the amount of product formed throughout the reaction. Using thisinformation, the initial quantity of target present in the reaction maybe determined. Additional information describing the principles andapplications of quantitative PCR can be found in: Real Time QuantitativePCR, Genome Research, Cold Spring Harbor Laboratory Press, 1996 and PCRTechnology: Principles and Applications for DNA Amplification. KarlDrlica, John Wiley and Sons, 1997.

One characteristic feature of quantitative PCR-based amplification isthat, the reaction kinetics typically change over the course of thereaction with the amount of product formed not necessarily increasing ina constant manner. For example, during the earlier cycles of a PCRreaction there may be an approximate doubling of the nucleotide strandswith each cycle (exponential amplification). In the later cycles of thereaction, however, the efficiency of the amplification process may bediminished resulting in non-exponential amplification. Some of thefactors that may affect the amplification efficiency include limitingquantities or depletion of reagents and competition for reactionproducts. The aforementioned changes in reaction kinetics may result indifficulties in determining the initial target concentration withoutperforming detailed analysis of the reaction profile. In one aspect, itis desirable to monitor the reaction at various time or cycle intervalsand acquire data which quantifies the emitted fluorescence of thereaction at these intervals. Using this information, data analysismethods may be used to assess the acquired fluorescence measurements anddetermine the initial concentration of target present in the reaction.

In quantitation methodologies, including real-time PCR, the fluorescenceintensity for each amplification reaction may be determined using acharge-coupled device (i.e. CCD camera or detector) or other suitableinstrument capable of detecting the emission spectra for the labelmolecules used in the reporter construct. Fluorescence samplings areperformed over the course of the reaction and may be made at selectedtime intervals (for example: 25 millisecond samplings performed at8.5-second intervals). In one aspect, emission spectra are measured forboth the label molecule and the quencher molecule with the emissionintensity resultant from the quencher molecule changing only slightlycompared to that of the label molecule. The emission intensity of thequencher molecule may further be used as an internal standard tonormalize emissions generated by the label molecule.

For each amplification reaction, the measured emission spectra obtainedfrom the fluorescence samplings form an amplification data set that maybe processed to determine the initial target concentration. In oneaspect, the amplification data set comprises fluorescence intensityinformation obtained from a plurality of independent or coupledreactions. These reactions may be performed simultaneously or atdifferent times wherein the data is accumulated and collectivelyanalyzed. Furthermore, the amplification data set may further comprisefluorescence intensity data obtained from one or more standards whoseinitial target concentration is known.

As will be described in greater detail with reference to the Figuresbelow, the methodologies presented herein may be applied to determinethe concentration of target present in each reaction prior toamplification. It will be appreciated that while described in thecontext of PCR-based amplification reactions and data, the analysisprocedures may be applied of other nucleic acid amplificationmethodologies such as Nucleic Acid Sequence Based Amplification (NASBA).Additionally, the target nucleotide sequence whose initial concentrationis to be determined may comprise nucleic acid sequences including DNA,cDNA, RNA, cRNA or any combination thereof and may be present as singleor double stranded nucleotide species. Furthermore, other types orconfigurations of reporter constructs may be similarly adapted for usewith the methods described herein including radiolabeled andchemiluminescent constructs, as well as other labeling constructs thatare detectable over the course of the amplification of the target.

FIG. 1 illustrates an amplification plot 105 depicting the reactioncharacteristics for an exemplary nucleic acid target and the variousanalytical components that may be used to quantify the target. It willbe appreciated that the amplification plot 105 is shown for the purposesof explanation and need not necessarily be constructed directly to applythe quantitative methods of the invention. However, the system can beconfigured to present a graphical representation of the amplificationdata set to aid a user in visualizing the results of the analysis.

The amplification plot 105 comprises a plurality of data points 107forming an amplification profile 117 which is indicative of the measuredintensity of signal generated by the label molecules within theamplification reaction. In the amplification plot 105, the y-axis values110 correspond to observed signal intensities generated over the courseof the amplification reaction. In one aspect, these signal intensitiesmay correspond to fluorescent emissions obtained from instrumentalsampling using a charge-coupled device or similar apparatus.Furthermore, the fluorescence detector may be configured to monitorwavelengths from approximately 500 to 650 nm. The x-axis values 115correspond to the sample interval (shown as a function of cycle number)for the amplification reaction for which the signals are observed.Illustrated in this manner, the information represents the reactionprogression as a function of the observed fluorescence intensities overthe sampling interval and may be used to monitor the synthesis ofprogeny nucleic acid strands from an initial sample target.

When analyzing the amplification profile 117, various regions may bedesirably identified that are subsequently used in calculations fordetermining the initial concentration of target present in the reaction.A common limitation of conventional analysis methodologies is arequirement for at least a degree of subjective interpretation.Oftentimes, a user must visually inspect the intensity data from a dataset in order to identify relevant regions of the amplification profile117 which are to be used in subsequent quantitative analysis. Thissubjective manner of manual analysis is undesirable and may decrease theaccuracy of the quantitation results, as well as, increase the analysistime.

In one aspect, the system and methods described herein overcome some ofthe limitations and drawbacks associated with conventional methodologiesthrough the implementation of an analysis strategy that identifiessignificant regions of the amplification profile 117 in an objective andreproducible manner. As a result, the invention may improve the accuracyof quantification when determining the initial concentration of targetpresent in an amplification reaction.

As shown by way of example in FIG. 1, the results from a typicalquantitation reaction can be characterized by different regions 120,125, 130 within, the amplification profile 117 corresponding to abaseline (noise) region 120, an exponential region 125, and a plateauregion 130. During the earlier cycles of the reaction, the observedfluorescence produced by the label generally does not substantiallyexceed that produced by the quencher. Fluorescent emissions measuredduring these cycles are generally very low and may fall below thedetection limits or sensitivity of the data acquisition instrumentation.Furthermore, within this region 120 non-specific florescence arisingfrom instrumental variations or noise may significantly contribute tothe observed signal. As a result, in the early cycles of the reaction itmay be difficult to accurately determine the emission fluorescencearising from true products of amplification, which may not be readilydistinguishable from background and/or non-specific fluorescence presentduring detection. It is therefore desirable to identify reactionfluorescence data in the background region 120 to avoid inaccuracies inquantitation which may arise if these values are inappropriately used toperform the analysis. Furthermore, during the quantitative analysis itmay be desirable to identify the range and bounds of the backgroundregion 120 so that this portion of the amplification reaction may bedistinguished from other regions of the amplification profile 117 wheredetected fluorescence may more accurately reflect the fluorescence ofthe desired products of the reaction.

In order to characterize the background region 120 for the purposes ofanalysis, a sub-region within the background region 120 may further beidentified as a baseline data set 122. The baseline data set 122 servesas an indicator of the relative level of background fluorescence ornoise from which the exponential region 125 may be differentiated. Inone aspect, a linear regression analysis may be performed on thebaseline data set 122 to identify a baseline 123 which can be describedby a characteristic equation used to evaluate the amplification data. Aswill be described in greater detail herein below, construction of thebaseline 123 provides a means to quantify the relative noise present inthe amplification reaction. Furthermore, the baseline 123 may be used tonormalize the data points 107 of the amplification profile 117 in orderto at least partially compensate for the noise.

In order to normalize data points 107, which lie outside of thebackground region 120, the baseline 123 may be extended using thecharacteristic equation. In one aspect, the characteristic equationcomprises a one-degree equation, which describes the baseline propertiesand can be extended to the terminal data point of the amplificationreaction. The extended baseline 124 can therefore be configured to spansubstantially the entire amplification profile or a portion thereof tofacilitate normalization of all data points 107 within the amplificationprofile 117. By taking the difference between the observed intensity (R⁺_(n)) 111 of each data point 107 within the amplification profile 117and the calculated intensity (R⁻ _(n)) 112 of the baseline 123 (orextended baseline 124) at the appropriate interval, a normalizedintensity value (ΔR_(n)) 113 may be obtained. Using this information, anormalized data set may be generated (data not shown), and used insubsequent quantitation of the target in a manner that will be discusseddetail with reference to FIG. 4 below.

The exponential region 125 comprises a region of the amplificationprofile 117 following the background region 120 where data points 107generally exhibit a trend of substantially increasing or progressivefluorescence. It is within this portion of the amplification profile 117where the observed intensity of fluorescence generally better correlatewith an exponentially increasing sample concentration with each cycle.Within the exponential region 125, the detected quantity of fluorescenceis typically sufficient to overcome noise that may predominate in thebackground region 120. The characteristics of the amplification reactionduring the cycles associated with the exponential region 125 furtherreflect desirable reaction kinetics that can be used to performquantitative target calculations.

It will be appreciated that the increase in target concentration withinthe exponential region 125 need not necessarily follow a substantiallyexponential rate. Instead, this region 125 of the amplification profile117 may be substantially characterized by a sub-exponential, geometric,linear and/or progressive rate of increase in target concentration. Moregenerally, the amplification region 125 may be characterized as theportion of the amplification profile 117 where an increased rate oftarget accumulation may be observed relative to earlier and later cyclesof the reaction. It will be appreciated that the methods describedherein are suitable for assessing amplification reactions having a widevariety of characteristic increases in target concentration and are notlimited exclusively to assessing regions of “pure” exponential increase.

In certain embodiments, an advantage of the present invention is theability to evaluate the exponential region 125 in an automated manner.In one aspect, exponential region evaluation comprises determining anupper bound 145 and lower bound 147 which delineate the approximatelimits of the exponential region 125. This information is subsequentlyused to identify the bounds of the baseline region 120, calculate thebaseline 123, and extend baseline 124. Additional details of thesemethods will be described in greater detail in subsequent illustrationsand discussion.

As shown in FIG. 1, the exponential region 125 may be followed by aplateau region 130 where the reaction ceases to increase in anexponential manner. Typically, the plateau region 130 occurs in thelater cycles of the reaction as the amplification reaction transitionsout of the exponential region 125. When performing quantitationcalculations, it is useful to distinguish the exponential region 125from the plateau region 130 to avoid erroneous or non-representativequantitation values. As with distinguishing the background region 120from the exponential region 125, the methods described herein similarlydistinguish the plateau region 130 from the exponential region 125 whichmay help to improve the quality of the resultant calculations that makeuse of this information.

Although the delineation of discrete regions within the amplificationprofile 117 is useful for distinguishing characteristic reactionkinetics and further identifying portions of the amplification profileamenable to quantitation calculations, it will be appreciated by one ofskill in the art that specific designation of these regions is notrequired to perform the quantitative calculations described herein. Itwill further be appreciated that the characteristics of these regionsmay vary from one reaction to the next and may deviate significantlyfrom illustrated profile. For example, in some amplification reactions,the exponential region 125 may extend over a different range of cyclesand possess different intensity characteristics. Likewise, thebackground region 120 and the plateau region 130 may possess uniquecharacteristics for each reaction. Additionally, other regions withinthe amplification profile 117 may be identifiable, for example, a regionof substantial linearity may follow the exponential region 125. As willbe described in greater detail herein below, the quantitation methodsmay be desirably “tuned” or customized to accommodate potentiallydiverse classes of amplification profile characteristics.

The analytical approach used to quantitate the initial targetconcentration is based, in part, upon the identification of a threshold135. In one aspect, the threshold 135 desirably aids in identifying anddelineating noise present in the background region 120 and furthermoreintersects with the amplification profile 117 at some point. The pointof intersection between the threshold 135 and the amplification profile117 is identified by a threshold cycle 140 (C_(T)) which isrepresentative of a cycle number associated with the point ofintersection. As will be appreciated by one of skill in the art,identification of the threshold cycle 140 is desirable as this value maybe used in subsequent calculations to predict the initial quantity orconcentration of target present in the reaction.

FIG. 2 illustrates one embodiment of a method 200 that may be used toanalyze amplification data to provide information which is useful inperforming quantitation calculations. In one aspect, the method may beadapted to operate in real-time PCR processes wherein quantitationcalculations are performed using intensity data collected at varioustimes throughout the course of an amplification reaction. It will beappreciated, however, that this method may be adapted to other types ofamplification reactions and is therefore not limited exclusively toanalysis of data in real-time or quantitative PCR.

The method 200 commences in state 210 with the amplification of a targetin the PCR reaction. As previously described, during amplification, areporter construct or probe may be incorporated into the contents of thereaction to provide a means for monitoring the reaction progression. Inone aspect, the reporter construct comprises a probe that fluoresces inrelative proportion to the quantity of progeny molecules synthesizedduring the amplification reaction.

During amplification of the target, intensity data or fluorescencemeasurements are acquired in state 220. Typically, the intensitymeasurements are made over a selected number of sampling intervals whichallow the progression of the amplification reaction to be monitored andassessed. In various embodiments, the sampling interval may berepresentative of the progression of the reaction measured as a functionof cycle number or time. For example, PCR-based amplification reactionstypically proceed according to pre-selected temperature-dependantprograms comprising cyclic variations in temperature which occur overone or more designated time intervals. In one aspect, the number ofcyclic variations in temperature for which the amplification reaction issubjected to defines the overall course of the reaction. Therefore, theamplification reaction may be conveniently subdivided according to thenumber of cycles used in the amplification reaction or alternatively oneor more designated time intervals may be used as a means to distinguishthe reaction progression.

Acquisition of intensity data or fluorescence measurements may likewisebe flexibly determined. Additionally, intensity measurements may beacquired to generally coincide with the cycles of the reaction.Collectively, the acquired intensity data for the reaction define thedata points 107 that reflect the amplification profile 117characteristic of each reaction. It will be appreciated that theaforementioned manner of data acquisition based on cycle number or timeis not rigidly defined and may be readily varied without departing fromthe scope of the invention. For the purposes of illustration anddiscussion, however, the intensity measurements for the amplificationdata are presented in terms of cycle number.

Thereafter, in state 230 the exponential region 125 of the amplificationreaction is determined by identification of the region's upper and lowerbounds. As will be described in greater detail herein below, the upperbound 145 is first determined through a derivatization process in whichthe fluorescence intensity data points are transformed so as to identifya transition point between the exponential region 125 and the plateauregion 130. Thereafter, the lower bound 147 of the exponential region125 is determined by incrementally assessing the data points 107 thatfall below the identified upper bound 145.

Following, exponential region identification, the process 200 proceedsto a state 240 where a baselining operation is performed. In one aspect,the baselining operation comprises identifying the bounds of thebaseline region 120 and performing a linear interpolation to identifythe characteristic equation defining the baseline 123 which passesapproximately through the data points 107 of the baseline region 120.The bounds of the baseline 123 can be determined, in part, byidentification of the bounds of the exponential region 125. In oneaspect, the identified lower bound 147 of the exponential region 125indicates the approximate upper bound of the baseline region 120.Furthermore, the approximate lower bound of the baseline region 120 maybe defined by the start cycle of the reaction or a selected number ofcycles (or a designated interval) from the start cycle. In oneembodiment, the lower bound of the baseline region 120 may be designatedas the data point 107 corresponding to the second cycle of theamplification profile 117.

In various embodiments, the linear interpolation utilized in baselineconstruction comprises performing a linear regression analysis for twoor more data points 107 contained within the baseline region 120 toidentify the characteristic baseline equation that can be “fit” to thedata points 107 of the baseline region 120. Thereafter, the baseline 123may be extended 124 out to the terminal cycle of the amplificationreaction. In one aspect, identification and extension of the baseline inthis manner provides a means for determining the relative noise ornon-specific fluorescence present in the intensity data. Using thebaseline 123 and extended baseline 124 as a reference, the amplificationdata may be processed so as to substantially remove the noise componentfrom each fluorescence data point 107 to generate a normalized data setfrom the original data.

Following baseline determination in state 240, the method 200 proceedsto state 250 where the threshold 135 is identified. Thresholdidentification may incorporate a data smoothing function as well as apolynomial equation/root identification function to define anappropriate threshold 135 and threshold cycle 140 for each amplificationreaction. As will be described in greater detail herein below, thethreshold identification process utilizes the upper and lowerexponential region bounds 145, 147 to approximate one or moreamplification profiles or curves that are fit along various portions ofthe exponential region.

By evaluating these curves with respect to one another, a polynomialequation can be identified that describes the characteristics of atleast a portion of the profile 117. In one aspect the “real” root of thepolynomial equation may be found to identify the threshold cycle 140.The threshold cycle 260 may then be used in subsequent calculations toquantitate the concentration of target present in the initial reaction.

Unlike conventional methods which subjectively assess the amplificationdata to identify the threshold cycle 260, various embodiments of thepresent invention provide a means for more rapidly and reproduciblyidentifying exponential and baseline regions of the amplificationprofile 117 to facilitate subsequent identification of the threshold 135and threshold cycle 140. Utilizing this method 200 may further improvethe accuracy and reproducibility of the analysis and reduce or eliminatethe need to visually inspect the intensity data which might otherwiseintroduce an undesirable subjective bias into the analysis.

Furthermore, in various embodiments, the methodologies described hereinmay be advantageously integrated into software applications and/orcomputer hardware so as to perform the baseline determination in asubstantially automated manner without the requirement of userintervention. This inventive feature may therefore improve theperformance of PCR-based quantitation and provide more rapididentification of initial target concentrations as compared to otherless efficient conventional analysis methodologies.

FIG. 3A illustrates one embodiment of a method 300 for exponentialregion identification. In one aspect, this method operates using a dataset comprising intensity information obtained from one or moreamplification reactions. Using the acquired intensity information, thismethod 300 desirably identifies the bounds of the exponential region 125of the amplification profile 117. In one aspect, the exponential regionidentification method 300 comprises a series of steps directed towardsapproximating the upper bound 145 of the exponential region 125. Theexponential region identification method 300 further approximates thelower bound 147 of the exponential region 125. The lower bound 147 ofthe exponential region 117 may additionally be used in baseliningoperations as will be described in greater detail in conjunction withsubsequent illustrations.

The method 300 commences in state 310 with the acquisition ofamplification data comprising the fluorescence information or intensitydata from the amplification reaction(s). Upon acquisition of the desiredintensity data, the method 300 proceeds to state 320 where aderivatization operation is performed on the intensity data associatedwith each amplification reaction. Derivatization of the fluorescenceintensities may be conveniently used to generate new representations ofthe data and facilitate identification of important amplificationprofile characteristics. In various embodiments, the derivatizationoperation further comprises calculating a first and second derivativefor the intensity data associated with each amplification reaction. Inthe context of analysis of the amplification profile, determination ofthe first derivative of the intensity data may be used to identify therelative length of the exponential region 125. Furthermore,determination of the second derivative of the intensity data may be usedto identify the theoretical upper bound 145 for each amplificationprofile 117.

In one aspect, the calculated second derivative of the intensity datagenerates a representation of the data comprising a plurality of“peaks”. Relating these peaks to the progression (cycle number) of theamplification reaction provide a means for identifying the upper bound145 of each exponential region 125. These peaks and their correspondingvalues are identified in state 330 and subsequently in state 340 thevalues for each peak are compared against a derivative selection value342. In various embodiments, the derivative selection value 342represents an empirically determined value based on the characteristicsof the amplification reaction and/or the instrumentation used in theanalysis. For example, in real-time PCR applications using a fluorescentreporter, a derivative selection value 342 in the range of approximately0.001 and 0.01 may be selected for use with some nucleic acid analysisinstrumentation. It will be appreciated that the derivative selectionvalue 342 need not conform to the above-indicated values and may readilybe re-defined to accommodate the characteristics of otherinstrumentation, reaction components, and/or reaction conditions.

When comparing each peak against the derivative selection value 342 instate 340, those peaks whose value does not exceed the derivativeselection value 342 may be removed from subsequent analytical steps. Inone aspect, peak selection in this manner desirably defines a minimumintensity criterion for determining the exponential region 125 of theamplification profile 117. Use of the derivative selection value 342therefore reduces the likelihood the inappropriate values will beidentified as the upper and lower bounds 145, 147 of the exponentialregion 125. While such a selection routine is desirable for many typesof analysis, it will be appreciated that the method 300 may be adaptedto not require the removal of peaks below the derivative selection value342 and thus the operations of state 340 may be optional in someembodiments of the exponential region identification method 300.

In state 350, a maximal peak 357 in the derivatized amplificationprofile is determined. In one aspect, the maximal peak 357 isrepresentative to the upper bound 145 of the exponential region 125 andthe location where this maximal peak 357 is found may be identified bythe approximate cycle number corresponding to this value. Followingidentification of the exponential region upper bound 145, the method 300proceeds to a series of steps wherein the lower bound 147 of theexponential region 125 is identified 355.

Identification of the lower bound 145 of the exponential region 125 isperformed in a loop-wise manner by incrementally identifying intensitydifferences between each cycle commencing substantially near the upperbound 145 of the exponential region 125 in state 360 and determining ifthe difference falls below a selected intensity difference value 372 instate 370. In one aspect, once the top of the exponential region isfound, cycle differences are identified between each cycle travelingbackwards towards cycle 1. At each cycle, a comparison of the ratio ofthe intensity at the current cycle versus the cycle ahead of it is made.If the ratio is smaller than a predetermined ratio, then the start cycleof the exponential region may be assigned to the cycle identified bythis comparison. In another aspect, the intensity difference iscalculated by identifying a cycle pair 362 comprising two consecutivedata points 107 starting from the upper bound 145 of the exponentialregion 125 and proceeding towards the first cycle of the amplificationreaction. The difference in intensities determined for the cycle pair362 is then compared to the selected intensity difference value 372. Ifthe calculated intensity difference of the cycle pair 362 does not fallbelow the selected intensity difference value 372, then the method 300loops back to state 360 where a new cycle pair 362 is selected and itsintensity difference determined.

The new cycle pair 362 is found by identifying a data point 107 thatprecedes the cycle pair 362 whose difference was previously determinedand using this value in place of the maximal value in the cycle pair362. In this manner, intensity differences between successive cyclepairs 362 are determined starting from the upper bound of theamplification region 125 until an intensity difference is calculatedwhich is below the selected intensity difference value 372. The cyclepair 362 whose intensity difference does not exceed the selectedintensity difference value 372 is identified and thereafter the lowerbound 147 of the exponential region 125 is equated to the minimalintensity value of the cycle pair 362 in state 380.

In various embodiments, the aforementioned intensity difference value372 is empirically determined and may be dependent upon characteristicsof the instrumentation, reagents and/or reaction conditions in a mannersimilar to the derivative selection value 342 described above.Furthermore, an intensity difference value 372 in the range ofapproximately 0.001 and 0.01 may be selected for use with some nucleicacid analysis instrumentation.

Using the aforementioned method 300, the exponential region 125 of anamplification profile 117 may be determined without the need forsubjective analysis. Additionally, this method may be readily adaptedfor use in software based analysis approaches to facilitate automatedprocessing of the amplification data with little or no userintervention. Another desirable feature of this method 300 is thatexponential region identification is generally reproducible and maycontribute to increased accuracy in subsequent analytical processes usedin the identification of the initial target concentration.

FIGS. 3B-3D illustrate the application of the exponential regionidentification method 300 using exemplary data shown in graphical form.It will be appreciated that the system and methods described herein donot require graphs to be generated during the analysis; however,graphical representation of the data can be performed to facilitate uservisualization of the analysis and results. As such, the graphicalrepresentation of amplification data as described herein is provided forthe purposes of exemplifying various features of the amplificationprofile that may be desirably identified during the analysis and shouldnot be interpreted to limit the scope of the invention.

In FIG. 3B, intensity data from a plurality of amplification reactionsthat are to be collectively analyzed is plotted as a function of cyclenumber. This data reflects one embodiment of the type of informationwhich may be collected in state 310 of the method 300. As previouslydescribed, the earlier reaction cycles may comprise a region ofvariability corresponding to the noise or background region 120. Thebackground region 120 is subsequently followed by the exponential region125 wherein the observed intensity of fluorescence in each reactionincreases in a relatively exponential or geometric manner. Thecalculated threshold 135 for the data is further illustrated asintersecting the amplification profiles to thereby allow determinationof the threshold cycle in a manner that will be described in greaterdetail herein below.

FIG. 3C illustrates a randomly selected amplification profile 117 fromthe plurality of amplification profiles shown in FIG. 2B above. Thecentral region of the amplification profile 117 is representative of theexponential region 125 and the fractional cycle number indicated by thepoint of intersection between the threshold 135 and the amplificationprofile 117 is designated to be the threshold cycle 140. For thepurposes of this illustration, the threshold cycle 140 is determined toreside between approximately cycle ‘25’ and cycle ‘26’. It will beappreciated however, that the value of the threshold cycle 140 isdependent upon the data represented by the amplification profile 117 andtherefore is not limited explicitly to the value indicated in theillustrated example.

FIG. 3D illustrates an exemplary representation of the intensity datagraphed as a function of cycle number following the second derivativeoperation performed in state 320 of the method 300. Upon obtaining thesecond derivative for the intensity data for each of the reactions, aplurality of peaks are formed. Comparison of the peaks against the peakselection value 342 may be performed as described in state 340 of themethod 300 wherein those peaks which do not exceed the peak selectionvalue 342 are removed from subsequent analysis. In one aspect, peaksremoved in this manner may represent amplification data that is notreadily distinguishable from background fluorescence or noise andtherefore are may not provide accurate quantitation results insubsequent analysis.

Further analysis of the peaks formed using the second derivativeoperation results in the identification of the maximal peak 357 for eachamplification reaction as described in state 350 of the method 300. Aspreviously indicated, the maximal peak 357 may be associated with theupper bound 145 of the exponential region 125 for a particularamplification reaction and serves as a reference point in subsequentlower bound identification 355.

Following exponential region identification, a baseline determinationmethod may be applied to the intensity data for each amplificationreaction in the data set. FIG. 4 illustrates a method for baselineanalysis 400 which utilizes the previously determined informationrelating to the identification of the lower bound 355 of the exponentialregion 125. In one aspect, this method 400 desirably approximates noiseor non-specific fluorescence present within the amplification reactionso that it may be removed from the amplification intensity data tothereby improve the quality of the quantitation. The method 400commences in state 410 wherein a linear regression is performed on thedata points 107 between the approximate beginning of the amplificationreaction and the lower bound 147 of the exponential region 125. Thelinear regression operation serves to identify the characteristicequation that describes the baseline 123 for the amplification profilewhich, in one aspect, is based upon a “best-fit” approach.

In various embodiments, this method establishes the baseline 123 whichcorresponds to a line segment that is fit between the intensity databetween the selected start cycle (typically cycle 2) and the lower bound147 of the exponential region 125. In one aspect, the characteristicequation comprises a one-degree polynomial equation that describes thebaseline 123. The characteristic equation may then be evaluated overeach cycle to generate the corresponding baseline value for a particularcycle or time interval of the amplification reaction. Using thisapproach, the baseline 123 is extended 124 through all of the cycles ofthe amplification data in state 420. Baseline extension in this mannermay therefore be used to approximate the amount of noise present withinthe data during each cycle of the amplification reaction.

In state 430, data corresponding to a normalized amplification profileis generated by subtracting the baseline value (determined from thecharacteristic equation) from the measured intensity data for each cyclein the amplification profile to generate the normalized amplificationprofile. In the normalized amplification profile, the intensitycomponent that arises from identified noise is substantially removed. Aspreviously described, noise may be introduced into the intensity data ina variety of manners and may include for example, instrumental noise andvariabilities, background fluorescence evolved from the reagents of theamplification reaction, and other types of non-specific fluorescencethat are detected by the instrumentation during data acquisitionprocess. The data and information corresponding to the normalizedamplification profile is subsequently returned in state 440 and may beused in threshold analysis as will be described in greater detail hereinbelow.

FIG. 5 illustrates one embodiment of a method for threshold analysis 500that may be used with amplification intensity data corresponding to oneor more reactions. In one aspect, this method 500 is desirably used inconjunction with amplification intensity data that has been previouslynormalized according to the exponential region identification andbaseline determination methods 300, 400. Although the method 500 isconfigured for use with amplification data normalized using above theabove-described methods 300, 400, it will be appreciated that otherforms of raw and normalized data may also be used with the thresholdanalysis process 500.

The threshold determination process 500 commences in state 510 byreceiving the normalized amplification data corresponding to one or morereactions that are to be desirably analyzed as an ordered set orcollection. The normalized amplification data comprises intensityinformation collected over a plurality of cycles for each amplificationreaction, as well as information regarding the upper and lower bounds ofeach amplification profile 117. In state 520, a minimal amplificationreaction having the smallest exponential region upper bound isidentified from the ordered set. Furthermore, in state 530, an maximalamplification reaction having the largest exponential region lower boundis identified from the ordered set.

Subsequently, in state 540 a comparison is made between the values ofthe identified upper and lower bounds 145, 147. If the results of thiscomparison indicate that the smallest identified upper bound is largerthan the largest identified lower bound then the method 500 proceeds tostate 550 indicating that an intersection region is observed. From thisdetermination, in state 560 the threshold 135 is assigned as the valueof the smallest identified upper bound of the ordered set.

Otherwise, in state 540 if the results of the comparison between thevalues of the identified upper and lower bounds indicate that thesmallest identified upper bound is smaller than the largest identifiedlower bound then the method 500 proceeds to state 570 indicating that nointersection region is observed in the current iteration. In state 580,if the current number of amplification reactions in the ordered setcorrespond to a single amplification reaction then the method 500proceeds to state 590 where the threshold 135 is assigned as the upperbound of the exponential region of the remaining amplification reaction.Alternatively, if more than one amplification reaction resides in theordered set, then the method 500 proceeds to state 595 where the minimalamplification reaction is removed from the ordered set and thereafterthe method proceeds to state 520 where a new minimal amplificationreaction is selected. The newly selected minimal amplification reactioncorresponds to the reaction whose lower bound exceeds that of the otherreactions within the ordered set (from which the former minimalamplification reaction has been removed). Thereafter, the method 500proceeds as before, resulting in the comparison between the values ofthe newly identified upper and lower bounds. This process continuesuntil a threshold 135 has been assigned in either state 560 or state590. Additional details of the threshold determination process will bedescribed in reference to FIG. 7 (below).

FIG. 6 illustrates one embodiment of a threshold cycle selection process600 that may be used for determining the threshold cycle (C_(T)) 140. Inone aspect, the method 600 utilizes the threshold 135 previouslydetermined in threshold analysis procedure 500 described in conjunctionwith FIG. 5 above. The method 600 commences in state 610 where aterminal cycle is identified. The terminal cycle is typically selectedas the endpoint of the amplification reaction (cycle 40 in theillustrated amplification plot shown in FIG. 1), however, it will beappreciated that designation of the terminal cycle may be substantiallyany value within the plateau region 130 or the exponential region 124 ofthe amplification profile 117. In state 610, a current comparison cycleis selected by decrementing one cycle from the terminal cycle. Thefluorescence intensity value of the current comparison cycle is thencompared to the value of the threshold 135 in state 630.

If the current comparison cycle is determined to be greater than thethreshold 135 then the method 600 loops back to state 620 where thecycle is again decremented to determine the next current comparisoncycle. In this manner, the method 600 incrementally compares each datapoint 107 with the threshold 135 until a data threshold point is foundhaving an intensity less than the threshold 135. The method 600 thenproceeds to state 640 where a determination is made as to the positionof the data threshold point within the amplification profile 117. In oneaspect, this state 640 verifies that the threshold data point fallswithin an acceptable range of the amplification profile 117. Here arange validation operation may be performed which comprises determiningif the threshold data point resides within a selected range from theterminal PCR cycle. In one aspect, the selected range may be determinedby assessing if the threshold data point is greater than a minimum cyclenumber (for example greater than a minimum cycle number of 3, 4, 5, 6,or 7) and furthermore if the threshold data point is less than aselected number of cycles away from the terminal PCR cycle (for exampleless than 3, 4, 5, 6, or 7 cycles away from the terminal cycle).

The range determination and verification made in state 640 helps avoidanomalous data points, which might otherwise lead to potentiallyinaccurate quantitation results. If the threshold data point isdetermined not to meet the criteria set forth in state 640 then themethod 600 proceeds to state 650 where the analysis is terminated forthe particular amplification reaction undergoing analysis. In oneaspect, if the intensity data for the amplification reaction does notmeet these criteria then the resulting amplification profile 117 isconsidered suspect and the reaction is flagged as potentially anomalousor erroneous. In this manner, the method 600 may identify anomalousamplification reactions whose confidence level for accurate quantitationis diminished based on the characteristics of the intensity data.

In one aspect, the value of the minimum cycle number and the value ofthe selected cycle number away from the terminal cycle are empiricallydetermined. For certain instrumentation and reaction compositions, theminimum cycle number may be selected to correspond to a cycle numberbetween approximately 3-7 which is desirably selected in combinationwith a selected cycle number of approximately 3-7 cycles from theterminal PCR cycle.

If the threshold data point passes the aforementioned criteria set forthin state 640, then the method 600 proceeds to state 660 where apolynomial fitting procedure is implemented to find an equation whichcan be fit to the amplification reaction data. In one aspect, thepolynomial fitting procedure comprises identifying a polynomial equationwhich starts a predetermined number of cycles above and below where thethreshold data point was selected in state 640 above. For example, inone implementation, upon identifying the threshold data point in state640, a 3rd degree polynomial is fit over the amplification reaction datastarting a selected number of cycles above and below where the datapoint 107 was identified.

It will be appreciated that the polynomial equation that is fit to theamplification profile may be of varying degrees and need not necessarilybe limited exclusively to a 3rd degree polynomial. Additionally, theposition at which the polynomial equation is fit to the amplificationprofile may be similarly varied and therefore need not necessarily belimited exclusively to a fixed number of cycles above and below thethreshold data point identified in state 640. In general, the polynomialfitting operations serve to smooth the data in the locality of thethreshold data point. In one aspect, the polynomial fitting operationscomprise an implementation of the Savitzky-Golay method for smoothing.Details of this method are described in detail in Numerical Recipes,Press et al. 1992.

Following polynomial fitting in state 660, the method 600 proceeds tostate 670 where the threshold 135 is subtracted from the constantportion of the polynomial equation (i.e. the Y-intersection coefficient)and the roots of the polynomial are determined. Based on the identifiedroots of the polynomial equation, a determination is made as to whetheror not a real root for the polynomial equation exists in state 680. Ifno real root exists, the method proceeds to state 650 where the analysisis terminated for the amplification reaction and the reaction dataflagged to indicate a possible anomalous or erroneous reaction. Ifhowever, the real root is determined to exist in state 680, then themethod proceeds to state 690 where the real root is associated with thethreshold cycle (C_(T)) 140 for the amplification reaction underanalysis.

Using the threshold cycle 140 identified using the method 600 describedabove, conventional quantitation procedures may be used to determine theinitial concentration of target present in the amplification reaction.For example, in various embodiments the threshold cycle (C_(T)) 140 maybe defined as a cycle or fractional cycle number at which the observedfluorescence intensity data of the amplification reaction passes theidentified threshold 135. Furthermore, quantitation of the amount oftarget in a sample may be accomplished by measuring the threshold cycle140 and using a standard curve constructed from reactions having knowntarget concentrations to determine the starting concentration or copynumber of the experimental target. It will be appreciated that theaforementioned methods advantageously perform the threshold cycledetermination with little or no required user input or decision making.As a result, subjective variability in the quantitative analysis ofPCR-based amplification data may be substantially removed. Furthermore,as previously described, analysis of the amplification data in theaforementioned manner may advantageously improve the degree of accuracyand reproducibility of the experimental analysis, as well as identifyanomalous or erroneous amplification reactions which might otherwiselead to inaccurate quantitation results.

FIGS. 7A-D further illustrates one embodiment of the aforementionedmethods for threshold determination wherein a plurality of amplificationcurves or profiles 702-705 are analyzed as a single ordered set 706. InFIG. 7A, the plurality of amplification profiles 702-705 correspondingto predicted exponential regions are shown as vertical lines. Eachamplification profile 702-705 comprises an upper bound 710 and a lowerbound 715. The bounds 710, 715 for each amplification profile 702-705are determined according to the exponential region identification method300 (shown in FIG. 3A). Proceeding through the threshold analysis 500(shown in FIG. 5), the method 500 first collectively evaluates the upperbounds 710 for the amplification profiles 702-705. From this assessment,the smallest upper bound 720 of the ordered set 706 is identified. In asimilar manner, the lower bounds 715 for the amplification profiles702-705 are collectively evaluated to determine the highest lower bound725 for the ordered set 706.

If the smallest upper bound 720 is determined to be greater in magnitudeor intensity than the largest lower bound 725 then an intersection 730between the amplification profiles 702-705 is determined to exist. Inthis instance, the threshold 735 is assigned to the greater of the twolimits corresponding to the smallest identified upper bound 720, asshown in FIG. 7B. Additionally, the threshold 135 delineates the upperbound of a threshold region 737 which is further bounded by the largestlower bound 725.

The threshold cycle (C_(T)) may then be determined by evaluating thecycle at which the threshold 735 intersects with the amplificationprofiles 702-705 of the ordered set 706. As previously described, thismethod of threshold cycle determination may be readily automated anddoes not require significant user interpretation or assessment.

FIG. 7C illustrates the occurrence when an intersection point is notfound between the amplification profiles 702-705 of the ordered set 706.In this instance, the lowest upper bound 720 does not intersect with thehighest lower bound 725. Accordingly, as described in the thresholdanalysis method 500, amplification profiles are incrementally discardeduntil intersection criteria are met.

As shown in FIG. 7D, applying the intersection criteria to theamplification profiles 703-705 illustrated in FIG. 7C results in thediscarding of two amplification profiles 703, 705 from the ordered set706. Of the remaining amplification profiles 703, 705, an intersectionpoint between the lowest upper bound 720 and the highest lower bound 725can be obtained which is designated as the threshold 135. Followingthreshold identification, an intersection region can be observed similarto that found in FIG. 7B above. Using this information, the thresholdcycle is likewise obtained and subsequently used in quantitationcalculations.

It will be appreciated that the threshold 735 assignment may bedetermined in a number of different ways upon identification of theintersection region 730 and is therefore not limited solely toassignment as the smallest upper bound 720. For example, in anotherembodiment, the threshold 735 may be assigned to the highest lower bound725. Alternatively, the threshold 735 may be assigned to a value midwaybetween the bounds 720, 725. In these and other embodiments, theassigned threshold 135 functions in substantially the same manner as theabove-described threshold assignment method. Taken together, thesemethods of threshold assignment provide a degree of flexibility whereinthe value of the threshold 735 may be varied based upon a desiredassignment criteria to yield different stringencies for determining thethreshold cycle (C_(T)).

FIG. 8 illustrates a system 800, according to various embodiments, forperforming quantitative PCR in conjunction with the aforementionedbaseline and threshold analysis methodologies. In one aspect, the system800 comprises a plurality of modules interconnected or networked by wayof a communications medium to substantially automate the analysis. Areaction module 810 receives the samples to undergo amplification andprovides the necessary hardware to regulate the temperature of thesamples in a desired manner. For example, reaction module 810 maycomprise a thermocycler or other hardware device capable of beingprogrammed with a particular method which defines controlled heating andcooling steps executed over designated time intervals.

The system 800 further comprises, in various embodiments, a datacollection module 820 that detects and measures the fluorescencegenerated for each amplification reaction. The data collection module820 may be configured to read the fluorescence directly while thereaction module 810 is in operation or alternatively samples from theamplification reactions may be withdrawn and measured separately by thedata collection module 820. In one aspect, the data collection module820 comprises a fluorescence detector configured to measure fluorescenceat the emission wavelength for a particular label or reporterincorporated into the amplification reaction.

The data collection module 820, according to various embodiments, cantransmit the fluorescence data to a data storage module 830 responsiblefor archiving the fluorescence results for each reaction over thespecified time course. The data storage module 830 may store the data innumerous different forms and configurations including tables, charts,arrays, spreadsheets, databases, and the like. In one aspect, the datastorage module 830 receives the results from many different experimentsand presents the data to other modules responsible for the subsequentcomparison and analysis of the data. Furthermore, the data storagemodule 830 stores the results of the quantitation analysis which may beoutput as needed or requested.

A data processing module 840, according to various embodiments, receivesselected data from the data storage module 830 or alternatively from thedata collection module 820 and performs the operations associated withnoise determination and threshold selection. These analytical methodsmay be implemented using one or more computer program or modules whichcomprise functions designed to manipulate the data and generaterequested information including: baseline noise level determination,exponential region identification, threshold selection and combination,quantitative analysis, and other related analytical methods. In oneaspect, the data processing module 840 is designed to operate in auser-independent manner where all of the calculations and analyticaltasks are performed without the need for the user to manually assess orinterpret the data.

Finally, in certain embodiments, a control module 850 may beincorporated into the system 800 to provide a means for integrating thetasks associated with each module. The control module 850 may beconfigured to communicate with each module of the system 800 andcoordinates system-wide activities to facilitate the automatedquantitative PCR analysis. Additionally, the control module 830 maymonitor each module to verify their proper function and provide a userinterface for interacting with the various components of the system 800.

FIG. 9 illustrates an exemplary code construction 900 comprisingpseudo-code for various functions related to the determination of thethreshold 135 and threshold cycle 140. In one aspect, a plurality ofmodules 910 are used to perform the threshold 135 and threshold cycle140 identification operations which pass data and parameters 920 betweenone another to coordinate the calculations. It will be appreciated thatthe illustrated code construction 900 represents but one embodiment ofhow the aforementioned methods may be implemented and other programmaticschemas may be readily utilized to achieve similar results. As such,these alternative schemas are considered to be but other embodiments ofthe present invention.

Although the above-disclosed embodiments of the present invention haveshown, described, and pointed out the fundamental novel features of theinvention as applied to the above-disclosed embodiments, it should beunderstood that various omissions, substitutions, and changes in theform of the detail of the devices, systems, and/or methods illustratedmay be made by those skilled in the art without departing from the scopeof the present invention. Consequently, the scope of the inventionshould not be limited to the foregoing description, but should bedefined by the appended claims.

All publications and patent applications mentioned in this specificationare indicative of the level of skill of those skilled in the art towhich this invention pertains. All publications and patent applicationsare herein incorporated by reference to the same extent as if eachindividual publication or patent application was specifically andindividually indicated to be incorporated by reference.

1. A method for threshold determination during target sequencequantitation, comprising: identifying an exponential region for each ofa plurality of target sequence amplifications, wherein each identifiedexponential region comprises an associated upper bound and an associatedlower bound; and determining an exponential region threshold based upona comparison of the exponential regions for each of the plurality oftarget sequence amplifications wherein the associated upper and lowerbounds for each identified exponential region are evaluated in thecomparison of the exponential regions.
 2. The method of claim 1, furthercomprising identifying a threshold cycle for each target sequenceamplification determined using the exponential region threshold.
 3. Themethod of claim 2, further comprising quantifying each of the pluralityof target sequences using the threshold cycle.
 4. The method of claim 2,further comprising performing a polynomial fitting operation to fit apolynomial to at least an interval of the exponential region includingthe exponential region threshold for each of the target sequenceamplifications.
 5. The method of claim 4, wherein identifying athreshold cycle comprises calculating a root of the polynomial equation,the root being associated with the threshold cycle.
 6. The method ofclaim 1, wherein determining the exponential region threshold comprises:identifying a minimal upper bound from the upper bounds; identifying amaximal lower bound from the lower bounds; identifying the intersectionbetween the minimal upper bound and maximal lower bound as theexponential region threshold.
 7. The method of claim 1, wherein theidentification of the exponential region lower bound comprises detectingincremental differences in the signal information starting at theexponential region upper bound of each of the target sequenceamplifications.
 8. The method of claim 7, wherein the incrementaldifferences comprise at least one of differences in a ratio of detectedintensity of successive values of the signal information and differencesin detected intensity of successive values of the signal information. 9.A method for target sequence quantitation, comprising: amplifying atleast one target sequence in the presence of a detectable reporterconstruct; acquiring signal information for the at least one targetsequence generated by detecting the associated detectable reporterconstruct; identifying an exponential region associated with substantialincreases in the signal information for the at least one target sequenceamplification, the exponential region comprising an upper bound and alower bound; determining an exponential region threshold based on acomparison of the upper bound and lower bound; performing a polynomialfitting operation to fit a polynomial to at least an interval of theexponential region including the exponential region threshold; andidentifying a threshold cycle for the at least one target sequenceamplification by calculating a root of the polynomial.
 10. The method ofclaim 9, wherein identification of the exponential region upper boundcomprises performing a derivative operation.
 11. The method of claim 10,wherein the derivative operation comprises obtaining a second derivativeusing the signal information.
 12. The method of claim 11, wherein theidentification of the exponential region lower bound comprises detectingincremental differences in the signal information starting at theexponential region upper bound.
 13. The method of claim 12, wherein theincremental differences comprise at least one of differences in a ratioof detected intensity of successive values of the signal information anddifferences in detected intensity of successive values of the signalinformation.
 14. The method of claim 9, wherein the polynomial fittingoperation comprises: performing a polynomial fitting operation over aselected number of cycles, including the threshold cycle, to identify athreshold equation; factoring the threshold equation to identify a realroot of the threshold equation; and associating the real root with thethreshold cycle.
 15. The method of claim 14, wherein the polynomialfitting operation comprises applying a Savitzky-Golay smoothingoperation.
 16. A system for threshold determination during targetsequence quantitation, comprising: a data collection module thatcollects signal information including detected reporter labelintensities generated by amplifying a plurality of target sequences inthe presence of a detectable reporter construct; and a data processingmodule configured to identify an exponential region for each of aplurality of target sequence amplifications, wherein each identifiedexponential region comprises an associated upper bound and an associatedlower bound; and determine an exponential region threshold based upon acomparison of the exponential regions for each of the plurality oftarget sequence amplifications wherein the associated upper and lowerbounds for each identified exponential region are evaluated in thecomparison of the exponential regions.
 17. The system of claim 16,wherein the data processing module is further configured to identify athreshold cycle for each target sequence amplification determined usingthe exponential region threshold.
 18. The system of claim 17, whereinthe data processing module is further configured to quantify each of theplurality of target sequences using the threshold cycle.
 19. The systemof claim 16, wherein the data processing module is further configured toperform a polynomial fitting operation to fit a polynomial to at leastan interval of the exponential region including the exponential regionthreshold for each of the target sequence amplifications.
 20. The systemof claim 17, wherein identifying a threshold cycle comprises calculatinga root of the polynomial equation, the root being associated with thethreshold cycle.
 21. The system of claim 16, wherein determining theexponential region threshold comprises: identifying a minimal upperbound from the upper bounds; identifying a maximal lower bound from thelower bounds; identifying the intersection between the minimal upperbound and maximal lower bound as the exponential region threshold. 22.The system of claim 16, wherein the identification of the exponentialregion lower bound comprises detecting incremental differences in thesignal information starting at the exponential region upper bound ofeach of the target sequence amplifications.
 23. The system of claim 22,wherein the incremental differences comprise at least one of differencesin a ratio of detected intensity of successive values of the signalinformation and differences in detected intensity of successive valuesof the signal information.
 24. A system for target sequencequantitation, comprising: a data collection module that collects signalinformation including detected reporter label intensities generated byamplifying at least one target sequence in the presence of a detectablereporter construct; and a data processing module configured to identifyan exponential region associated with substantial increases in thesignal information for the at least one target sequence amplification,the exponential region comprising an upper bound and a lower bound,determine an exponential region threshold based on a comparison of theupper bound and lower bound, perform a polynomial fitting operation tofit a polynomial to at least an interval of the exponential regionincluding the exponential region threshold, and identify a thresholdcycle for the at least one target sequence amplification by calculatinga root of the polynomial.
 25. The system of claim 24, whereinidentification of the exponential region upper bound comprisesperforming a derivative operation.
 26. The system of claim 24, whereinthe polynomial fitting operation comprises: performing a polynomialfitting operation over a selected number of cycles, including thethreshold cycle, to identify a threshold equation; factoring thethreshold equation to identify a real root of the threshold equation;and associating the real root with the threshold cycle.